[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82011-en":3,"doc-seo-82011-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82011,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","AI-Driven Thermal Mapping and Management in 3D Integrated Photonic Circuits","Photonic Integrated Circuits (PICs) are accelerating high-performance computing, data centers, and sensing, but 3D PICs face severe thermal management issues from high-density bonding and heterogeneous materials. Conventional thermal microscopes and in-package sensors provide sparse measurements, preventing complete temperature-profile visibility. The paper introduces a dual-method approach: an AI-driven thermal modeling framework that fuses sparse sensor inputs with design-layer and density information, and a design-based heuristic refinement using local materials, geometry, and sensor coordinates to generate accurate multilayer thermal maps.","AI-Driven Thermal Mapping and Management in 3D Integrated  \nPhotonic Circuits  \nLiton Kumar Biswas 1, Katayoon Yahyaei 1, Shajib Ghosh 1, M Shafkat M Khan 1, Himanandhan Reddy Kottur 1, Rayhane Ghane-Motlagh2, Mahdi Nikdast3, Navid Asadizanjani 1 1University of Florida, Gainesville, Florida 32611  \n2ficonTEC Service USA Inc., FL, USA 3Colorado State University, Fort Collins, Colorado 80523 Email: [litonkumarbiswas@ufl.edu](litonkumarbiswas@ufl.edu)  \nAbstract  \nPhotonic Integrated Circuits (PICs) are advancing high-performance computing, data centers, and sensing, yet three-dimensional (3D) PICs introduce critical thermal management challenges due to high-density bonding and heterogeneous materials. Traditional methods like thermal microscopes and in-package sensors yield sparse data, limiting full thermal profile visibility. This paper presents a dual-method solution combining an AI-driven thermal modeling framework with a design-based heuristic approach. The AI method integrates sparse sensor data with design layer and density information to predict multilayer temperature variations, while the heuristic approach uses localized material properties, design layout, component geometries, and sensor coordinates to refine thermal estimations in specific regions. A 2D thermal map of a 3D PIC is generated by interpolating sensor data and adjusting for local thermal resistivity using comparative analysis between design regions. The heuristic method complements the AI model, improving estimation accuracy without extensive training data. Together, these methods offer a scalable, accurate solution for real-time thermal mapping and design-time simulation, enabling reliable thermal management in next-generation 3D photonic systems.  \nKey words  \nSilicon photonics; 3D heterogeneous integration, PICs, thermal mapping, Artificial intelligence, thermal assurance.  \nI. Introduction  \nPhotonic integrated circuits (PICs) pack many optical components – lasers, modulators, detectors, waveguides – onto a single chip, enabling ultra‑fast, energy‑efficient data communication [1,2] . Light carries data with minimal loss, revolutionizing long‑haul fiber communications and promising to “greatly expand computing power” in data centers and AI systems if on‑chip optical interconnects can be realized. Silicon photonics, leveraging mature CMOS fabrication, already provides modulators, filters, and detectors that achieve hundreds of gigabits per second per channel at low energy [2] . However, packing photonics and electronics side‑by‑side on a flat (2D) chip has limitations. For example, co‑integrating electronics and photonics on one die “freezes” the electronics at a given technology node and limits density. At high integration scale, planar PICs require numerous waveguide crossings, which introduce optical loss and crosstalk and also exhaust chip area.  \nTo overcome these limits, three-dimensional (3D) heterogeneous integration is emerging as the next frontier. In  \n3D PICs, multiple functional layers – electronic circuits, optical sources or gain layers, passive waveguide layers, etc.  \n– are vertically stacked and coupled [3] . This stacking enables much higher device density and new functionality, for example, 3D space‑division multiplexing and beam steering. Early 3D photonic‑electronic systems have already demonstrated interconnect energy below 200 fJ/bit, leveraging separate optimized chips bonded together [2]. By separating the electronic driver (on an advanced CMOS chip) from the photonic layer, designers can use the latest transistors while using silicon or III–V materials optimally for optics. Such heterogeneous 3D architectures promise orders of magnitude more optical channels and modes than planar PICs, with tighter integration and co‑packaged optics. However, stacking layers with diverse materials and devices also creates a critical thermal management challenge. Every active photonic and electronic component generates heat, and in a dense 3D s","cbCailx66JJF7Kca","https://ap.wps.com/l/cbCailx66JJF7Kca","pdf",896855,1,6,"English","en",105,"# Abstract\n# I. Introduction\n# II. Background","[{\"question\":\"What core thermal-management problem do 3D PICs face compared with planar designs?\",\"answer\":\"3D stacking vertically combines heterogeneous materials and dense bonded layers, forcing heat to flow through thin silicon, dielectric, and metal layers. This creates temperature gradients that can shift resonant wavelengths, reduce efficiency, and cause thermal crosstalk between channels.\"},{\"question\":\"How does the AI-driven thermal modeling framework improve thermal estimation?\",\"answer\":\"It integrates sparse sensor data with design-layer and density information to predict multilayer temperature variations. This enables thermal profile prediction even when measurements are limited.\"},{\"question\":\"What does the design-based heuristic method add to the AI approach?\",\"answer\":\"It refines thermal estimates in specific regions using localized material properties, design layout, component geometries, and sensor coordinates. It improves estimation accuracy without requiring extensive training data.\"}]",1784177558,15,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"ai-driven-thermal-mapping-and-management-in-3d-integrated-photonic-circuits","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/ai-driven-thermal-mapping-and-management-in-3d-integrated-photonic-circuits/82011/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What core thermal-management problem do 3D PICs face compared with planar designs?","Question",{"text":75,"@type":76},"3D stacking vertically combines heterogeneous materials and dense bonded layers, forcing heat to flow through thin silicon, dielectric, and metal layers. This creates temperature gradients that can shift resonant wavelengths, reduce efficiency, and cause thermal crosstalk between channels.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the AI-driven thermal modeling framework improve thermal estimation?",{"text":80,"@type":76},"It integrates sparse sensor data with design-layer and density information to predict multilayer temperature variations. This enables thermal profile prediction even when measurements are limited.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the design-based heuristic method add to the AI approach?",{"text":84,"@type":76},"It refines thermal estimates in specific regions using localized material properties, design layout, component geometries, and sensor coordinates. 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